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Neo4j, A Graph Database For Building Recommendation Engines, Gets A Visual Overhaul

Neo4j, A Graph Database For Building Recommendation Engines, Gets A Visual Overhaul

Part of the problem with any powerful technology is how it is perceived. It might be something that is too early for its time or it may just need those years of development and use for the market to catch up to its potential. That is true of graph databases like Neo4j, which now has a new graphical interface that helps people map relationships between different people, places or things.
There is one simple way to think about graph databases, said Emil Eifrem, CEO of Neo Technology and one of the original developers of the graph database technology. And that is to explore how graph databases treat relationships as first-class citizens. The knowledge is all about relationships. When more connections are made, the view fills out. Graph databases find the relationships, the missing pieces that help form connections.
Graphs databases are still known by relatively few people, but they are gaining acceptance as the use cases increase. The reasons are clear when you consider how much data is now getting created. With that scaling comes a growing demand for new types of analytics capabilities.
Graph databases are becoming more popular for the varied amounts of data they aggregate and analyze. They treat everything as a node. That might be things like a street light or people. The properties of a graph database describe the nodes. A graph database also has “edges” that connect the nodes and properties, defining the relationship between them. The value is derived when analyzing the patterns between the nodes and the properties.
Neo Technology has put a heavy emphasis on the user interface to make it more accessible and easier to query and build graphs from large data sets. It took the developers 10 years to get to Neo4j 1.0, said Eifrem. They built it to be fully transactional and ACID compliant, meaning transactions are processed reliably. But it lacked a decent user interface. So that became the focus as they developed the 2.0 release.
In this new version, the goal is to make it possible for developers to create their own recommendation engines using visual guides and simple queries. Cypher, the query language, has been updated and streamlined, making it more accessible to someone like a business analyst. It also now supports labels that refer to subsets of nodes in a graph, introducing a form of schema into the technology. This means that the data can be indexed better, allowing the developer to tell the database more about the data.
Read the full article.